Job Title: Databricks Architect
Experience: 10+ Years
Location: Hyderabad
Employment Type: Full-Time
Job Summary
We are looking for an experienced Databricks Architect to design, develop, and implement scalable data platforms and modern data solutions on Databricks. The ideal candidate should have strong expertise in data architecture, cloud technologies, big data processing, and data engineering best practices. The candidate will work closely with business stakeholders, data engineers, and leadership teams to define and implement enterprise-wide data strategies.
Key Responsibilities
Design and architect scalable data platforms using Databricks and cloud technologies.
Define end-to-end data architecture, including ingestion, transformation, storage, and consumption layers.
Lead the implementation of Lakehouse architecture using Databricks.
Design and optimize ETL/ELT pipelines for large-scale data processing.
Establish data governance, security, and compliance standards.
Work with business stakeholders to understand requirements and translate them into technical solutions.
Provide architectural guidance to development teams and review solution designs.
Optimize Databricks workloads for performance, scalability, and cost efficiency.
Implement CI/CD pipelines and Dev Ops best practices for data platforms.
Mentor data engineers and technical teams on Databricks best practices.
Collaborate with cross-functional teams including Data Science, Analytics, and Business Intelligence.
Required Skills
Strong experience in Databricks Architecture and Administration.
Expertise in Apache Spark (Py Spark/Scala).
Experience with Delta Lake , Unity Catalog , and Databricks Workflows.
Strong understanding of Lakehouse Architecture.
Hands-on experience with cloud platforms such as Azure, AWS, or GCP.
Experience designing and implementing enterprise data warehouses and data lakes.
Solid SQL and data modeling expertise.
Experience with data integration and orchestration tools.
Knowledge
📌 Data architect (Hyderabad)
🏢 Anblicks
📍 Hyderabad
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